Elasticsearch应用五：搜索详解（查询建议介绍、Suggester 介绍）

Bertha 。 2022-02-21 05:19 290阅读 0赞

## 一、查询建议介绍 ##

###  1. 查询建议是什么？ ###

查询建议，为用户提供良好的使用体验。主要包括： 拼写检查； 自动建议查询词（自动补全）

拼写检查如图：

![1227483-20180622211305578-841821106.png][]

自动建议查询词（自动补全）：

![1227483-20180622211334385-608756405.png][]

### 2. ES中查询建议的API ###

查询建议也是使用\_search端点地址。在DSL中suggest节点来定义需要的建议查询

示例1：定义单个建议查询词

POST twitter/_search
    {
      "query" : {
        "match": {
          "message": "tring out Elasticsearch"
        }
      },
      "suggest" : { 
        "my-suggestion" : { 
          "text" : "tring out Elasticsearch", 
          "term" : { 
            "field" : "message" 
          }
        }
      }
    }

示例2：定义多个建议查询词

POST _search
    {
      "suggest": {
        "my-suggest-1" : {
          "text" : "tring out Elasticsearch",
          "term" : {
            "field" : "message"
          }
        },
        "my-suggest-2" : {
          "text" : "kmichy",
          "term" : {
            "field" : "user"
          }
        }
      }
    }

示例3：多个建议查询可以使用全局的查询文本

POST _search
    {
      "suggest": {
        "text" : "tring out Elasticsearch",
        "my-suggest-1" : {
          "term" : {
            "field" : "message"
          }
        },
        "my-suggest-2" : {
           "term" : {
            "field" : "user"
           }
        }
      }
    }

## 二、Suggester 介绍 ##

### 1. Term suggester ###

term 词项建议器，对给入的文本进行分词，为每个词进行模糊查询提供词项建议。**对于在索引中存在词默认不提供建议词**，不存在的词则根据模糊查询结果进行排序后取一定数量的建议词。

常用的建议选项：

![1227483-20180622212230983-253626529.png][]

示例1：

###  2. phrase suggester ###

phrase 短语建议，在term的基础上，会考量多个term之间的关系，比如是否同时出现在索引的原文里，相邻程度，以及词频等

示例1：

POST /ftq/_search
    {
      "query": {
        "match_all": {}
      },
      
      "suggest" : {
        "myss":{
          "text": "java sprin boot",
          "phrase": {
            "field": "title"
          }
        }
      }
    }

结果1：

{
      "took": 177,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 2,
        "max_score": 1,
        "hits": [
          {
            "_index": "ftq",
            "_type": "_doc",
            "_id": "2",
            "_score": 1,
            "_source": {
              "title": "java spring boot",
              "content": "lucene is writerd by java"
            }
          },
          {
            "_index": "ftq",
            "_type": "_doc",
            "_id": "1",
            "_score": 1,
            "_source": {
              "title": "lucene solr and elasticsearch",
              "content": "lucene solr and elasticsearch for search"
            }
          }
        ]
      },
      "suggest": {
        "myss": [
          {
            "text": "java sprin boot",
            "offset": 0,
            "length": 15,
            "options": [
              {
                "text": "java spring boot",
                "score": 0.20745796
              }
            ]
          }
        ]
      }
    }

###  3. Completion suggester   自动补全 ###

针对自动补全场景而设计的建议器。此场景下用户每输入一个字符的时候，就需要即时发送一次查询请求到后端查找匹配项，在用户输入速度较高的情况下对后端响应速度要求比较苛刻。**因此实现上它和前面两个Suggester采用了不同的数据结构，索引并非通过倒排来完成，而是将analyze过的数据编码成FST和索引一起存放。对于一个open状态的索引，FST会被ES整个装载到内存里的，进行前缀查找速度极快。****但是FST只能用于前缀查找，这也是Completion Suggester的局限所在。**

官网链接：

[https://www.elastic.co/guide/en/elasticsearch/reference/current/search-suggesters-completion.html][https_www.elastic.co_guide_en_elasticsearch_reference_current_search-suggesters-completion.html]

为了使用自动补全，索引中用来提供补全建议的字段需特殊设计，字段类型为 completion。

PUT music
    {
        "mappings": {
            "_doc" : {
                "properties" : {
                    "suggest" : {  
                        "type" : "completion"
                    },
                    "title" : {
                        "type": "keyword"
                    }
                }
            }
        }
    }

Input 指定输入词 Weight 指定排序值（可选）

PUT music/_doc/1?refresh
    {
        "suggest" : {
            "input": [ "Nevermind", "Nirvana" ],
            "weight" : 34
        }
    }

指定不同的排序值：

PUT music/_doc/1?refresh
    {
        "suggest" : [
            {
                "input": "Nevermind",
                "weight" : 10
            },
            {
                "input": "Nirvana",
                "weight" : 3
            }
        ]}

放入一条重复数据

PUT music/_doc/2?refresh
    {
        "suggest" : {
            "input": [ "Nevermind", "Nirvana" ],
            "weight" : 20
        }
    }

示例1：查询建议根据前缀查询：

POST music/_search?pretty
    {
        "suggest": {
            "song-suggest" : {
                "prefix" : "nir", 
                "completion" : { 
                    "field" : "suggest" 
                }
            }
        }
    }

结果1：

{
      "took": 25,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 0,
        "max_score": 0,
        "hits": []
      },
      "suggest": {
        "song-suggest": [
          {
            "text": "nir",
            "offset": 0,
            "length": 3,
            "options": [
              {
                "text": "Nirvana",
                "_index": "music",
                "_type": "_doc",
                "_id": "2",
                "_score": 20,
                "_source": {
                  "suggest": {
                    "input": [
                      "Nevermind",
                      "Nirvana"
                    ],
                    "weight": 20
                  }
                }
              },
              {
                "text": "Nirvana",
                "_index": "music",
                "_type": "_doc",
                "_id": "1",
                "_score": 1,
                "_source": {
                  "suggest": [
                    "Nevermind",
                    "Nirvana"
                  ]
                }
              }
            ]
          }
        ]
      }
    }

示例2：对建议查询结果去重

POST music/_search?pretty
    {
        "suggest": {
            "song-suggest" : {
                "prefix" : "nir", 
                "completion" : { 
                    "field" : "suggest",
                    "skip_duplicates": true 
                }
            }    }}

结果2：

{
      "took": 4,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 0,
        "max_score": 0,
        "hits": []
      },
      "suggest": {
        "song-suggest": [
          {
            "text": "nir",
            "offset": 0,
            "length": 3,
            "options": [
              {
                "text": "Nirvana",
                "_index": "music",
                "_type": "_doc",
                "_id": "2",
                "_score": 20,
                "_source": {
                  "suggest": {
                    "input": [
                      "Nevermind",
                      "Nirvana"
                    ],
                    "weight": 20
                  }
                }
              }
            ]
          }
        ]
      }
    }

示例3：查询建议文档存储短语

PUT music/_doc/3?refresh
    {
        "suggest" : {
            "input": [ "lucene solr", "lucene so cool","lucene elasticsearch" ],
            "weight" : 20
        }
    }
    
    PUT music/_doc/4?refresh
    {
        "suggest" : {
            "input": ["lucene solr cool","lucene elasticsearch" ],
            "weight" : 10
        }
    }

查询3：

POST music/_search?pretty
    {
        "suggest": {
            "song-suggest" : {
                "prefix" : "lucene s", 
                "completion" : { 
                    "field" : "suggest" ,
                    "skip_duplicates": true
                }
            }
        }
    }

结果3：

{
      "took": 3,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 0,
        "max_score": 0,
        "hits": []
      },
      "suggest": {
        "song-suggest": [
          {
            "text": "lucene s",
            "offset": 0,
            "length": 8,
            "options": [
              {
                "text": "lucene so cool",
                "_index": "music",
                "_type": "_doc",
                "_id": "3",
                "_score": 20,
                "_source": {
                  "suggest": {
                    "input": [
                      "lucene solr",
                      "lucene so cool",
                      "lucene elasticsearch"
                    ],
                    "weight": 20
                  }
                }
              },
              {
                "text": "lucene solr cool",
                "_index": "music",
                "_type": "_doc",
                "_id": "4",
                "_score": 10,
                "_source": {
                  "suggest": {
                    "input": [
                      "lucene solr cool",
                      "lucene elasticsearch"
                    ],
                    "weight": 10
                  }
                }
              }
            ]
          }
        ]
      }
    }

[1227483-20180622211305578-841821106.png]: /images/20220221/410d6bb53457471aab4c07293ba1639c.png
[1227483-20180622211334385-608756405.png]: /images/20220221/587fa26925184ac68a885b1740b8b379.png
[1227483-20180622212230983-253626529.png]: /images/20220221/84533739c54848f0bcecc2cee1d112f5.png
[https_www.elastic.co_guide_en_elasticsearch_reference_current_search-suggesters-completion.html]: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-suggesters-completion.html